Article Structure

Abstract

Existing models for social media personal analytics assume access to thousands of messages per user, even though most users author content only sporadically over time.

Introduction

Inferring latent user attributes such as gender, age, and political preferences (Rao et al., 2011; Zamal et al., 2012; Cohen and Ruths, 2013) automatically from personal communications and social media including emails, blog posts or public discussions has become increasingly popular with the web getting more social and volume of data available.

Identifying Twitter Social Graph

Twitter users interact with one another and engage in direct communication in different ways e.g., using retweets, user mentions e.g., @youtube or hashtags e.g., #tcot, in addition to having explicit connections among themselves such as following, friending.

Batch Models

Baseline User Model As input we are given a set of vertices representing users of interest 2),- E V along with feature vectors f derived from content authored by the user of interest.

Streaming Models

We rely on straightforward Bayesian rule update to our batch models in order to simulate a real-time streaming prediction scenario as a first step beyond the existing models as shown in Figure 2.

Experimental Setup

We design a set of experiments to analyze static and dynamic models for political affiliation classification defined in Sections 3 and 4.

Conclusions and Future Work

In this paper, we extensively examined state-of-the-art static approaches and proposed novel models with dynamic Bayesian updates for streaming personal analytics on Twitter.

Topics

social media

Appears in 13 sentences as: social media (13)

In Inferring User Political Preferences from Streaming Communications

Existing models for social media personal analytics assume access to thousands of messages per user, even though most users author content only sporadically over time.

Page 1, “Abstract”

Inferring latent user attributes such as gender, age, and political preferences (Rao et al., 2011; Zamal et al., 2012; Cohen and Ruths, 2013) automatically from personal communications and social media including emails, blog posts or public discussions has become increasingly popular with the web getting more social and volume of data available.

Page 1, “Introduction”

In this paper we analyze and go beyond static models formulating personal analytics in social media as a streaming task.

Page 1, “Introduction”

The proposed baseline model follows the same trends as the existing state-of-the-art approaches for user attribute classification in social media as described in Section 8.

Page 3, “Batch Models”

7We use log-linear models over reasonable alternatives such as perceptron or SVM, following the practice of a wide range of previous work in related areas (Smith, 2004; Liu et a1., 2005; Poon et a1., 2009) including text classification in social media (Van Durme, 2012b; Yang and Eisenstein, 2013).

Page 3, “Batch Models”

Following the streaming nature of social media , we see the scarce available resource as the number of requests allowed per day to the Twitter API.

log-linear

Our goal is assign to a category each user of interest 2),- based on f Here we focus on a binary assignment into the categories Democratic D or Republican R. The log-linear

Page 3, “Batch Models”

7We use log-linear models over reasonable alternatives such as perceptron or SVM, following the practice of a wide range of previous work in related areas (Smith, 2004; Liu et a1., 2005; Poon et a1., 2009) including text classification in social media (Van Durme, 2012b; Yang and Eisenstein, 2013).

log-linear models

Appears in 3 sentences as: log-linear model (1) log-linear models (2)

In Inferring User Political Preferences from Streaming Communications

7We use log-linear models over reasonable alternatives such as perceptron or SVM, following the practice of a wide range of previous work in related areas (Smith, 2004; Liu et a1., 2005; Poon et a1., 2009) including text classification in social media (Van Durme, 2012b; Yang and Eisenstein, 2013).